Breast tumor diagnosis using radiofrequency signals based ultrasound multifeature maps combined with radiomics analysis

Wang, Qingmin; Jia, Xiaohong; Xiao, Tianlei; Zhao, Yao; Zhou, Jianqiao; Yu, Jinhua

doi:10.1109/cisp-bmei53629.2021.9624456

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Previous studies [ 50 , 51 , 52 ] have shown a close association between radiomics and survival in patients with tumors, chronic obstructive pulmonary disease, and cancers. The patient outcome in this study is a manifestation of survival.…”

Section: Discussionmentioning

confidence: 99%

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

et al. 2022

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Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. Results: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. Conclusions: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.

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Section: Discussionmentioning

confidence: 99%